Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Modeling and Classifying Breast Tissue Density in Mammograms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Introduction to Information Retrieval
Introduction to Information Retrieval
Discriminative cue integration for medical image annotation
Pattern Recognition Letters
Medical image annotation in ImageCLEF 2008
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Overview of the CLEF 2009 medical image annotation track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Sparse patch-histograms for object classification in cluttered images
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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This work presents a novel approach to chest x-ray characterization. It is based on the generation of a visual words dictionary to represent x-ray images, and similarity-based categorization with a kernel based SVM classifier. Two main tasks are addressed: First, the extraction of chest images from a large radiograph archive, i.e. an organ identification task; Second, the detection and identification of chest pathologies, i.e. shifting from the organ level to a pathology level analysis. We used a large generic archive of 12,000 radiographs (IRMA) to tune the system parameters. We demonstrate automated organ detection on the IRMA collection as well as the generalization to a new data collection. The application is shown to discriminate between healthy and pathology cases, as well as identify specific pathologies on a set of 223 chest radiographs taken from a routine hospital examination. Results indicate detection of pathology at a sensitivity of 88.4% and a specificity of 81%. This is a first step towards similarity-based categorization that has a major clinical importance in computer-assisted diagnostics.